展示Arena 5.0：用于开发和基准测试社会导航的 photorealistic ROS2 仿真框架
Demonstrating Arena 5.0: A Photorealistic ROS2 Simulation Framework for Developing and Benchmarking Social Navigation
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中文总结 AI 辅助
本文介绍开源的 Arena 5.0 框架，该框架集成 NVIDIA Isaac Gym 实现 photorealistic 仿真，通过基准测试和用户研究验证其在社会导航开发与基准测试中较旧版本可用性、效率显著提升。
中文摘要 AI 辅助
在前期工作奠定的基础上，本文介绍了Arena 5.0，这是我们用于机器人社会导航开发和基准测试的框架的第五个版本。Arena 5.0 提供三项主要贡献：1）完整集成 NVIDIA Isaac Gym，实现 photorealistic 仿真和更高效的训练。它将 Isaac Gym 无缝融入 Arena 平台，允许使用现有模块，如随机环境生成、评估工具、ROS2 支持，以及 Isaac Gym 内规划器、机器人模型和 API 的集成。2）对最先进的社会导航策略进行全面基准测试，在一组多样化的生成和定制世界及不同难度级别的场景上进行评估。这些基准测试使用广泛的社会导航指标对导航规划器进行详细评估。3）用于改进和可定制生成社会导航场景（如紧急救援场景）的广泛场景生成和任务规划模块。通过生成上述基准测试和全面用户研究对平台性能进行评估，结果显示与旧版本相比，可用性和效率有显著提升。Arena 5.0 为开源项目，可通过此 https URL 获取。
英文摘要
Building upon the foundations laid by our previous work, this paper introduces Arena 5.0, the fifth iteration of our framework for robotics social navigation development and benchmarking. Arena 5.0 provides three main contributions: 1) The complete integration of NVIDIA Isaac Gym, enabling photorealistic simulations and more efficient training. It seamlessly incorporates Isaac Gym into the Arena platform, allowing the use of existing modules such as randomized environment generation, evaluation tools, ROS2 support, and the integration of planners, robot models, and APIs within Isaac Gym. 2) A comprehensive benchmark of state-of-the-art social navigation strategies, evaluated on a diverse set of generated and customized worlds and scenarios of varying difficulty levels. These benchmarks provide a detailed assessment of navigation planners using a wide range of social navigation metrics. 3) Extensive scenario generation and task planning modules for improved and customizable generation of social navigation scenarios, such as emergency and rescue situations. The platform's performance was evaluated by generating the aforementioned benchmark and through a comprehensive user study, demonstrating significant improvements in usability and efficiency compared to previous versions. Arena 5.0 is open source and available at https://github.com/Arena-Rosnav.
发表机构
- Technical University Berlin (TUB)（柏林工业大学）
- National University of Singapore (NUS)（新加坡国立大学）
- Technical University Munich (TUM)（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。
